Reference

Overview
abess-package _PACKAGE	abess: Fast Best Subset Selection
Generalized linear model
abess(<default>) abess(<formula>)	Adaptive best subset selection (for generalized linear model)
coef(<abess>)	Extract Model Coefficients from a fitted "abess" object.
deviance(<abess>)	Extract the deviance from a fitted "abess" object.
extract()	Extract one model from a fitted "abess" object.
plot(<abess>)	Creat plot from a fitted "abess" object
predict(<abess>)	Make predictions from a fitted "abess" object.
print(<abess>)	Print method for a fitted "abess" object
Principal component analysis
abesspca()	Adaptive best subset selection for principal component analysis
coef(<abesspca>)	Extract Sparse Loadings from a fitted "abesspca" object.
plot(<abesspca>)	Creat plot from a fitted "abess" object
print(<abesspca>)	Print method for a fitted "abesspca" object
Robust principal component analysis
abessrpca()	Adaptive best subset selection for robust principal component analysis
coef(<abessrpca>)	Extract sparse component from a fitted "abessrpca" object.
plot(<abessrpca>)	Creat plot from a fitted "abessrpca" object
print(<abessrpca>)	Print method for a fitted "abessrpca" object
Synthetic datasets
generate.data()	Generate simulated data
generate.matrix()	Generate matrix composed of a sparse matrix and low-rank matrix
generate.spc.matrix()	Generate matrix with sparse principal component
Real-world dataset
trim32	The Bardet-Biedl syndrome Gene expression data